Based on Muriel Lobier’s original code (https://github.com/FRCBS/iron_levels_of_blood_donors/blob/master/src/index.Rmd)

Summary

This document includes all codes necessary to run the analysis of and produce the figures for the three Finnish Cohorts (FinDonor, FINRISK97, Health2000). The code allows the user to describe the cohorts and build a summary table that can be used in further regression analysis.

knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(tableone)
library(GGally)
library(knitr)
library(kableExtra)
library(rmarkdown)

Data loading and preparation

Data loading

We have one cohort of blood donors, FinDonor, and two general population cohorts, FinRisk97 and Health2000.

# load FinDonor biomarker data
load("../data/r02.fd.bd.all.rdata")
indiv_donations_data <- output
rm(output)

# load FinDonor demographic data
load("../data/r02ds.donorData.rdata")
donor_demo <- output
rm(output)

# load THL data
load("../data/thldata.rdata")

# thldalta.rdata contains all five THL cohorts, extract FINRISK97 and Health2000 from the others 
fr1997 <- thldata$fr1997
h2000 <- thldata$h2000
rm(thldata)

Data preparation

First we assign study participants to their cohorts.

indiv_donations_data$Cohort <- c("FINDONOR")
fr1997$Cohort <- c("FINRISK97")
h2000$Cohort <- c("HEALTH00")

There are “cohort %>% distinct(ID_variable) %>% nrow()” individials enrolled in the studies.

FinDonor: 2584
FinRisk97: 7943
Health2000: 6264

Once we remove participants that have no ferritin or no Hb measurement data (and for blood donors for any donation event) there are “r cohort_name %>% filter(!is.na(Ferritin) & !is.na(Hb_v) ) %>% distinct(ID_variable) %>% nrow()” participants left

FinDonor: 2580
FinRisk97: 462
```
    * 7380
    * 491
```
Health2000: 5250

At this stage we noticed we only had hemoglobin data from 491 FinRisk97 participants. Using the table function (“table(fr1997\(HGB, fr1997\)SUKUP)”, SUKUP = sukupuoli = sex, 1=male, 2=female) we found out that all these participants are male. As such, we didn’t remove NA:s for Hb and will not use Hb in the model for this specific cohort.

#FINDONOR

## Get sex for each blood donor
blood_data_summary <- indiv_donations_data %>% 
  group_by(donor) %>% 
  summarise(Sex=first(gender)) 

## Get values for first study donation with the required measurements donation (regardless of donation type)
## We remove events with no Ferritin and hb_v to be inline with from when the nb of previous donations were counted.
blood_values_init <- indiv_donations_data %>% 
  filter(!is.na(Ferritin) & !is.na(Hb_v) ) %>% 
  group_by(donor) %>% 
  filter(date == min(date)) %>% 
  dplyr::select(age, Ferritin, Hb_v, CRP, DaysToPreviousFB, donor, Cohort) %>% 
  ungroup()

blood_data_summary <- blood_data_summary %>% 
    full_join(blood_values_init, by = "donor") 

blood_data_summary <- donor_demo %>%
  inner_join(blood_data_summary, by = "donor") %>%
  mutate(all_study_FB_donation_count = NonFinnDonorDonationCount_FB + YesFinnDonorDonationCount_FB) 

#FINRISK97 

## Change sex from binary 1,2 to men, women
fr1997$SUKUP <- gsub("1", "Men", fr1997$SUKUP)
fr1997$SUKUP <- gsub("2", "Women", fr1997$SUKUP)

fr1997_summary <- fr1997 %>%
  filter(!is.na(FERRITIN)) %>%
  group_by(RELEASE_ID) %>%
  dplyr::select(IKA, SUKUP, FERRITIN, HGB, CRP, RELEASE_ID, K129, BMI, KY100_22, KY163, GRAVID, Cohort, ALUE, PAINO, TUPI3) %>%
  ungroup()

#HEALTH2000
## Change sex from binary 1,2 to men, women
h2000$SP2 <- gsub("1", "Men", h2000$SP2)
h2000$SP2 <- gsub("2", "Women", h2000$SP2)  
  
 h2000_summary <- h2000 %>%
    filter(!is.na(FERRITIINI) & !is.na(B_Hb)) %>%
    group_by(RELEASE_ID) %>%
    dplyr::select(IKA2, SP2, FERRITIINI, B_Hb, CRP, RELEASE_ID, BD03, BMII_BMI.x, FB01, FB02, FB03, FB05, synnytys, BD07, Cohort, MENOP, BD06, MP_2000, BMII_PAINO.x) %>%
    ungroup()

Next we rename the variables.

## Participant ID 
names(blood_data_summary)[names(blood_data_summary) == "donor"] <- "ID"
names(fr1997_summary)[names(fr1997_summary) == "RELEASE_ID"] <- "ID"
names(h2000_summary)[names(h2000_summary) == "RELEASE_ID"] <- "ID"

## Sex
names(fr1997_summary)[names(fr1997_summary) == "SUKUP"] <- "Sex"
names(h2000_summary)[names(h2000_summary) == "SP2"] <- "Sex"

## Age
names(fr1997_summary)[names(fr1997_summary) == "IKA"] <- "Age"
names(h2000_summary)[names(h2000_summary) == "IKA2"] <- "Age"

## Hemoglobin
names(blood_data_summary)[names(blood_data_summary) == "Hb_v"] <- "Hb"
names(fr1997_summary)[names(fr1997_summary) == "HGB"] <- "Hb"
names(h2000_summary)[names(h2000_summary) == "B_Hb"] <- "Hb"

## Ferritin
names(fr1997_summary)[names(fr1997_summary) == "FERRITIN"] <- "Ferritin"
names(h2000_summary)[names(h2000_summary) == "FERRITIINI"] <- "Ferritin"

## Menstruation
names(blood_data_summary)[names(blood_data_summary) == "QR79"] <- "Menstruation"
names(fr1997_summary)[names(fr1997_summary) == "K129"] <- "Menstruation"
names(h2000_summary)[names(h2000_summary) == "BD03"] <- "Menstruation"

## BMI
names(h2000_summary)[names(h2000_summary) == "BMII_BMI.x"] <- "BMI"

## Smoking
names(blood_data_summary)[names(blood_data_summary) == "QR54"] <- "Smoking"
names(fr1997_summary)[names(fr1997_summary) == "TUPI3"] <- "Smoking"
names(h2000_summary)[names(h2000_summary) == "FB01"] <- "EverSmoked"
names(h2000_summary)[names(h2000_summary) == "FB02"] <- "Smoked100"
names(h2000_summary)[names(h2000_summary) == "FB03"] <- "RegSmoked"
names(h2000_summary)[names(h2000_summary) == "FB05"] <- "Smoking"

## Red meat
names(blood_data_summary)[names(blood_data_summary) == "QR40"] <- "RedMeat"
names(fr1997_summary)[names(fr1997_summary) == "KY100_22"] <- "RedMeat"

## Iron supplements
names(blood_data_summary)[names(blood_data_summary) == "vita_iron"] <- "HistoryOfIronSupplements"
names(blood_data_summary)[names(blood_data_summary) == "iron_supp"] <- "GivenIronSupplements"
names(blood_data_summary)[names(blood_data_summary) == "iron_comp"] <- "IronComplience"
names(blood_data_summary)[names(blood_data_summary) == "iron_comp_c"] <- "IronComplienceNumeric"

## History of childbirth
names(blood_data_summary)[names(blood_data_summary) == "QR83"] <- "PreviousChildbirth"
names(fr1997_summary)[names(fr1997_summary) == "KY163"] <- "PreviousChildbirth"
names(h2000_summary)[names(h2000_summary) == "synnytys"] <- "PreviousChildbirth"

## Current pregnancy
names(fr1997_summary)[names(fr1997_summary) == "GRAVID"] <- "CurrentPregnancy"
names(h2000_summary)[names(h2000_summary) == "BD07"] <- "CurrentPregnancy"

## Region
names(fr1997_summary)[names(fr1997_summary) == "ALUE"] <- "Region"
names(h2000_summary)[names(h2000_summary) == "MP_2000"] <- "Region"

## Weight
names(blood_data_summary)[names(blood_data_summary) == "weight"] <- "Weight"
names(fr1997_summary)[names(fr1997_summary) == "PAINO"] <- "Weight"
names(h2000_summary)[names(h2000_summary) == "BMII_PAINO.x"] <- "Weight"

## THESE ARE ONLY NEEDED FOR h2000 to figure out the n/a:s related to menstruation
names(h2000_summary)[names(h2000_summary) == "BD06"] <- "WhyEnd"

Demographic group specification and assignment (coninued after “figuring out why so many Health2000 participants are removed” section)

In this section we assign women to period/no period groups. The menstruation question was similar across all three cohorts:

FinDonor: Do you still have regular periods? (1=reg., 2=irreg., 3=no)
FinRisk97: Do you still menstruate? (1=reg., 2=irreg., 3=no)
Health2000: BD03 = do you have periods nowadays? (1=reg., 2=irreg., 3=no)

There are “r cohort_summary_name %>% filter(Sex ==”Women" & is.na(Menstruation)) %>% nrow()" women with no answer to the question regarding their menstrual status.

FinDonor: 17
FinRisk97: 41
Health00: 1029

Evidently we are missing menstruation data from >37 % of female Health2000 participants.

Figuring out why so many Health2000 participants are removed (skip this part unless there are problems with the cohort)

Menstruation was defined as: 1 = regular menstruation 2 = irregular menstruation 3 = no menstruation The cohort also contains a variable for reproductive stages, MENOP: 1 = postmenopause 2 = perimenopause 3 = premenopause

Removing n/a:s for the MENOP variable 48 would remove only 48 participants. In the next code chunk we investigate the difference between these variables in order to figure out which we can/should use.

In the home interview instruction (https://thl.fi/documents/189940/4108213/T2001_eng.pdf/cd17a5fe-ddf3-4649-9ddd-a282b1809de9, page 41) we find out that only women under the age of 55 yrs were asked about menstruation. This amounts to: * # NAs in the <55 yrs group 180 * # NAs in the >=55 yrs group 849

Our original plan was to place participants in pre- and postmenopausal groups based on menstrual status. Removing n/a:s based on the MENOP variable would remove fewer participants, however we are missing this data in the other cohorts. Next we try to find out how menstruation, reproductive stage and age align in the Health2000 cohort:

h2000_mens_missing <- h2000_summary 

# make new variables based on age (<45 yrs, >=45 yrs) and reproductive stage
h2000_mens_missing <- h2000_mens_missing %>% 
  mutate(AgeGroup = case_when(
    Sex == "Women" & (Age >= 45) ~ "older",
    Sex == "Women" & (Age < 45) ~ "younger",
    )) %>% 
  mutate(Stage = case_when(
    MENOP == "1" ~ "POSTmenopausal",
    MENOP == "2" ~ "PERImenopausal",
    MENOP == "3" ~ "PREmenopausal"
  )) %>% 
  mutate(MENSTRUATION = case_when(
    Menstruation == "1" ~ "regular",
    Menstruation == "2" ~ "irregular",
    Menstruation == "3" ~ "no"
  )) %>% 
  filter(Sex == "Women") 

## first we check  what data on reproductive state looks like 
h2000_mens_missing %>% 
  filter(is.na(Stage) ) %>% 
  nrow()

## [1] 48

h2000_missing_stage <- h2000_mens_missing %>% 
  filter(!is.na(Stage)) 

# check to see if recode worked
table(factor(h2000_missing_stage$Stage, levels = c("PREmenopausal", "PERImenopausal", "POSTmenopausal")), h2000_missing_stage$MENOP)

##                 
##                     1    2    3
##   PREmenopausal     0    0 1516
##   PERImenopausal    0  103    0
##   POSTmenopausal 1092    0    0

# yes, they were assigned to the right groups

# compare reproductive stage with reported bleeding in the two age groups (<45, >45)
table(factor(h2000_mens_missing$MENSTRUATION, levels = c("regular", "irregular", "no")), factor(h2000_mens_missing$Stage, levels = c("PREmenopausal", "PERImenopausal", "POSTmenopausal")), factor(h2000_mens_missing$AgeGroup, levels = c("younger", "older")))

## , ,  = younger
## 
##            
##             PREmenopausal PERImenopausal POSTmenopausal
##   regular             858              0              0
##   irregular           119              0              0
##   no                   11              6             57
## 
## , ,  = older
## 
##            
##             PREmenopausal PERImenopausal POSTmenopausal
##   regular             316              0              0
##   irregular           135              0              0
##   no                   16             38            160

# compare reported bleeding in the two age groups 
table(factor(h2000_mens_missing$MENSTRUATION, levels = c("regular", "irregular", "no")), factor(h2000_mens_missing$AgeGroup, levels = c("younger", "older")))

##            
##             younger older
##   regular       858   316
##   irregular     121   141
##   no             78   216

# compare reported reproductive stage in the two age groups 
table(factor(h2000_mens_missing$Stage, levels = c("PREmenopausal", "PERImenopausal", "POSTmenopausal")), factor(h2000_mens_missing$AgeGroup, levels = c("younger", "older")))

##                 
##                  younger older
##   PREmenopausal      995   521
##   PERImenopausal      11    92
##   POSTmenopausal      78  1014

## next we investigate how removing n/a:s only from women under 55 yrs compares to the above data (women older that 55 are considered postmenopausal)

women_older_than_55 <- h2000_mens_missing %>% 
  filter(Age >= 55) %>% nrow() 

h2000_mens_missing %>% 
  filter(Age < 55 & is.na(MENSTRUATION)) %>% 
  nrow()

## [1] 180

h2000_mens_missing %>% 
  filter(Age < 45 & is.na(MENSTRUATION)) %>% 
  nrow()

## [1] 43

women_older_than_55 <- h2000_mens_missing %>% 
  filter(Age >= 55) %>% nrow()  

women_older_than_55 <- filter(h2000_mens_missing,
                              Age >= 55 & is.na(Menstruation))$ID

test <- h2000_mens_missing %>% 
  mutate(Menstruation = ifelse(ID %in% women_older_than_55, "no", as.character(Menstruation)))

We will remove n/a:s from women under 55 and use the same strategy for assigning reproductive groups as we do for the other cohorts. According to THL the MENOP-variable has been constructed from reported menstruation and use of hormone therapy in the following manner: 1= postmenopause, time since last period >= 12 months 2= perimenoause, time since last period 6-12 months OR hormone replacement therapy, period had not stopped before start of use 3= premenopause, time since last period < 6 months 4= . = data missing 5= .X = participant was not asked (short forms)

Removing NAs for Menstruation as opposed to MENOP will remove more women, but at this point using the same way of filtering participants for all cohorts seems to be the best way to move forward. As we are also interested in menstruation as a variable in itself, we would still have to remove the participants with NAs for this particular question at a later stage.

Demographic group specification and assignment continued

Out of the women with no answer to the menstruation question, “r cohort_summary_name %>% filter(Sex ==”Women" & is.na(Menstruation) & age >= 55) %>% nrow()" are older than 55. We impute these to no period so they can be included in the postmenopausal women.

FinDonor: 3
FinRisk97: 32
Health2000: 849

# FINDONOR
older_findonor_donors <- filter(blood_data_summary, 
                       Sex == "Women" & is.na(Menstruation) & Age >= 55)$ID

blood_data_summary <- blood_data_summary %>% 
  mutate(Menstruation = ifelse(ID %in% older_findonor_donors, "no_period", as.character(Menstruation))) 

# FINRISK97
older_fr1997 <- filter(fr1997_summary, 
                       Sex == "Women" & is.na(Menstruation) & Age >= 55)$ID

fr1997_summary <- fr1997_summary %>% 
  mutate(Menstruation = ifelse(ID %in% older_fr1997, "no_period", as.character(Menstruation)))

# HEALTH2000
older_h2000 <- filter(h2000_summary,
                      Sex == "Women" & is.na(Menstruation) & Age >= 55)$ID

h2000_summary <- h2000_summary %>%
  mutate(Menstruation = ifelse(ID %in% older_h2000, "no_period", as.character(Menstruation)))

Female study participants with no menstruation response after imputation are removed: “cohort_name %>% filter(sex ==”Women" & is.na(Menstruation)) %>% nrow()" donors.)

FinDonor: 14
FinRisk97: 9
Health2000: 180

#FINDONOR
findonor_nb_women_no_menstruation_response <- blood_data_summary %>% filter(Sex == "Women" & is.na(Menstruation)) %>% nrow()

## remove female donors with no menstruation response
blood_data_summary_final <- blood_data_summary %>% 
  mutate(mens_ok_blood = case_when(
    Sex == "Men" ~ "Men", 
    Sex == "Women" & !is.na(Menstruation)  ~ "Women",
    TRUE ~ "NA")) %>% 
  filter(mens_ok_blood != "NA") 

## We define the women's groups: 
findonor_n_women_removed <- blood_data_summary_final %>% 
  mutate(Group = case_when(
    Sex == "Men" ~ "Men", 
    Sex == "Women" & (Menstruation == "no_period" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "no_period" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "irregular_period" | Menstruation == "regular_period") ~ "Pre_menopause_women",
    TRUE ~ "NA")) %>% 
  group_by(Group) %>% 
  filter(Group ==  "Women_pre_menop_no_mens") %>% 
  nrow()


#FINRISK97

finrisk_nb_women_no_menstruation_response <- fr1997_summary %>% filter(Sex == "Women" & is.na(Menstruation)) %>% nrow()


## remove female FinRisk97 participants with no menstruation response
fr1997_summary_final <- fr1997_summary %>% 
  mutate(mens_ok_fr1997 = case_when(
    Sex == "Men" ~ "Men", 
    Sex == "Women" & !is.na(Menstruation)  ~ "Women",
    TRUE ~ "NA")) %>% 
  filter(mens_ok_fr1997 != "NA") 

## We define the women's groups:
fr1997_n_women_removed <- fr1997_summary_final %>%
  mutate(Group = case_when(
    Sex == "Men" ~ "Men",
    Sex == "Women" & (Menstruation == "3" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "3" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "1" | Menstruation == "2") ~ "Pre_menopause_women",
    TRUE ~ "NA")) %>%
  group_by(Group) %>%
  filter(Group ==  "Women_pre_menop_no_mens") %>%
  nrow()

#HEALTH2000

h2000_summary <- h2000_summary %>%
  mutate(Menstruation = ifelse(ID %in% older_h2000, "no_period", as.character(Menstruation)))

h2000_nb_women_no_menstruation_response <- h2000_summary %>% filter(Sex == "Women" & is.na(Menstruation)) %>% nrow()

## remove female Health2000 participants with no menstruation response
h2000_summary_final <- h2000_summary %>%
  mutate(mens_ok_h2000 = case_when(
    Sex == "Men" ~ "Men",
    Sex == "Women" & !is.na(Menstruation)  ~ "Women",
    TRUE ~ "NA")) %>%
  filter(mens_ok_h2000 != "NA")

## We define the women's groups:
h2000_n_women_removed <- h2000_summary_final %>%
  mutate(Group = case_when(
    Sex == "Men" ~ "Men",
    Sex == "Women" & (Menstruation == "3" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "3" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "1" | Menstruation == "2") ~ "Pre_menopause_women",
    TRUE ~ "NA")) %>%
  group_by(Group) %>%
  filter(Group ==  "Women_pre_menop_no_mens") %>%
  nrow()

We now define the following groups:

premenopausal: regular or irregular menstruation reported
postmenopausal: no menstruation reported and age equal to > 45
donors younger than 45 and no reported menstruation are excluded (“cohort_n_women_removed”)
- FinDonor 77
- FinRisk 81
- Health2000 83

#FINDONOR:
## We define the women's groups and drop n/a:s: 
blood_data_summary_final <- blood_data_summary_final %>% 
  mutate(Group = case_when(
    Sex == "Men" ~ "Men", 
    Sex == "Women" & (Menstruation == "no_period" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "no_period" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "irregular_period" | Menstruation == "regular_period") ~ "Pre_menopause_women",
    TRUE ~ "NA")) %>% 
  filter(Group != "Women_pre_menop_no_mens" & Group != "NA") %>% 
  droplevels() %>% 
  mutate(Group = ordered(Group, levels =  c("Pre_menopause_women", "Post_menopause_women", "Men"))) 

#FINRISK97:
## We define the women's groups and drop n/a:s: 
fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(Group = case_when(
    Sex == "Men" ~ "Men", 
    Sex == "Women" & (Menstruation == "3" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "3" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "1" | Menstruation == "2") ~ "Pre_menopause_women",
    Sex == "Women" & (Menstruation == "no_period") ~ "Post_menopause_women",
    TRUE ~ "NA")) %>% 
  filter(Group != "Women_pre_menop_no_mens" & Group != "NA") %>% 
  droplevels() %>% 
  mutate(Group = ordered(Group, levels =  c("Pre_menopause_women", "Post_menopause_women", "Men"))) 
 
#HEALTH2000:

h2000_summary_final <- h2000_summary_final %>%
  mutate(Group = case_when(
    Sex == "Men" ~ "Men",
    Sex == "Women" & (Menstruation == "3" & Age > 45) ~ "Post_menopause_women",
    Sex == "Women" & (Menstruation == "3" & Age <= 45) ~ "Women_pre_menop_no_mens",
    Sex == "Women" & (Menstruation == "1" | Menstruation == "2") ~ "Pre_menopause_women", 
    Sex == "Women" & (Menstruation == "no_period") ~ "Post_menopause_women",
    TRUE ~ "NA")) %>%
  filter(Group != "Women_pre_menop_no_mens" & Group != "NA") %>%
  droplevels() %>%
  mutate(Group = ordered(Group, levels =  c("Pre_menopause_women", "Post_menopause_women", "Men")))

In order to later merge the three cohorts into one table, we need to make colums for the variables missing variables in each cohort and impute values for them.

The general population cohorts don’t contain any information on blood donor activity. As seen in a previous FinDonor study the effect of full blood donation on ferritin levels is marginal after 1000 days (see Lobier et al. 2019, https://pubmed.ncbi.nlm.nih.gov/31408501/). After referring to these results we decided to assign a randomized donation interval between three to six years for the general population cohorts.

# Add new columns and impute values for them

blood_data_summary_final$CurrentPregnancy <- 0

fr1997_summary_final$TwoYearsFromStartCount_FB <- 0
fr1997_summary_final$HistoryOfIronSupplements <- NA
fr1997_summary_final$GivenIronSupplements <- 0
fr1997_summary_final$IronComplience <- 0
fr1997_summary_final$IronComplienceNumeric <- 0

h2000_summary_final$TwoYearsFromStartCount_FB <- 0
h2000_summary_final$HistoryOfIronSupplements <- NA
h2000_summary_final$GivenIronSupplements <- 0
h2000_summary_final$IronComplience <- 0
h2000_summary_final$IronComplienceNumeric <- 0
h2000_summary_final$RedMeat <- NA

# We then  assign a randomized donation interval for 3-6 years for the general population cohorts 
fr1997_summary_final <- fr1997_summary_final %>% mutate(DaysToPreviousFB = round(runif(nrow(fr1997_summary_final), min = 3*365, max = 6*365) ,0))
h2000_summary_final <- h2000_summary_final %>% mutate(DaysToPreviousFB = round(runif(nrow(h2000_summary_final), min = 3*365, max = 6*365) ,0))

We also need to rename observations for the menstruation variable in general population participants in order to match blood donors.

fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(Menstruation = case_when(
    Sex == "Women" & (Menstruation == "3") ~ "no_period",
    Sex == "Women" & (Menstruation == "2") ~ "irregular_period",
    Sex == "Women" & (Menstruation == "1") ~ "regular_period",
    TRUE ~ "NA")) %>% 
  mutate(Menstruation = ordered(Menstruation, levels =  c("regular_period", "irregular_period", "no_period"))) 

h2000_summary_final <- h2000_summary_final %>% 
  mutate(Menstruation = case_when(
    Sex == "Women" & (Menstruation == "3") ~ "no_period",
    Sex == "Women" & (Menstruation == "2") ~ "irregular_period",
    Sex == "Women" & (Menstruation == "1") ~ "regular_period",
    Sex == "Women" & (Age >= 55 & Group == "Post_menopause_women") ~ "no_period",
    TRUE ~ "NA")) %>% 
  mutate(Menstruation = ordered(Menstruation, levels =  c("regular_period", "irregular_period", "no_period")))

Finally, we will need the smoking variables to just give us informaton on current smoking behaviour, not past smoking. This does not need to be done in the FinDonor cohort, as the variable gives us the information we want as it is.

FinRisk97: TUPI3 was the chosen variable. It’s a mutation built out of four separate variables in the FinRisk questionnaire: 1=Never smoked regurarly –> 0 2=Stopped smoking >1/2 years ago –> 0 3=Stopped smoking <1/2 years ago –> 0 4=Smokes –> 1

Health00:

For this cohort there was no mutated variable avaliable, so we had to build our own from the following avaliable questions:

FB01. Have you ever smoked during your life time? 1 yes 0 no → GA01 FB02. Have you smoked at least 100 times during your life time (cigarettes, cigars or pipe tobacco)? 1 yes 0 no → GA01 FB03. Have you ever smoked daily for at least one year? 1 yes 0 no → FB05 FB05. Do you smoke nowadays (cigarettes, cigars or pipe): 1 daily 2 occasionally 3 not at all

People who have never smoked (FB01=EverSmoked), have smoked <100 cigarettes in their lifetime (FB02=Smoked100), have not smoked daily for at least 1 year (FB03=SmokedReg) or smoke occasionally/not at all (FB05=Smoking) are imputed as non-smokers (0). Participants who report daily smoking (FB05=Smoking) are imputed as smokers (1).

# FinRisk97
fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(Smoking = case_when(
    Smoking == "4" ~ 1,
    Smoking %in% c("1", "2", "3") ~ 0))
  
# Health2000
h2000_summary_final <- h2000_summary_final %>% 
  mutate(Smoking = case_when(
            Smoking == "1" ~ 1,
            Smoking %in% c("2" , "3") ~ 0,
            EverSmoked == "0" ~ 0,
            Smoked100 == "0" ~ 0,
            RegSmoked == "0" ~ 0))

Remove cohort participants with missing data

Donation history

We remove 42 blood donors who have not donated previously (they are missing the number of days since last donation variable).

new_donors_data <- blood_data_summary_final %>% 
  filter(is.na(DaysToPreviousFB)) %>% 
    mutate(Group = case_when(Group == "Pre_menopause_women" ~ "Pre-menopausal women",
                   Group == "Post_menopause_women" ~ "Post-menopausal women",
                   Group == "Men" ~ "Men" ,
                   TRUE~ "NA"),
         Group = ordered(Group)) 

blood_data_summary_final <- blood_data_summary_final %>% 
  drop_na(DaysToPreviousFB)

BMI

We remove “cohort_name %>% filter(is.na(BMI)) %>% nrow()” participants for whom we do not have the BMI data: * FinDonor: 21 * FinRisk97: 8 * Health2000: 1

#### FinDonor
nb_removed_blood <- findonor_nb_women_no_menstruation_response +
                            blood_data_summary_final %>% filter(is.na(BMI)) %>% nrow() 

blood_data_summary_final <- blood_data_summary_final %>% 
  filter(!is.na(BMI))

#### FinRisk97
nb_removed_fr97 <- finrisk_nb_women_no_menstruation_response +
                            fr1997_summary_final %>% filter(is.na(BMI)) %>% nrow() 

fr1997_summary_final <- fr1997_summary_final %>% 
  filter(!is.na(BMI))

#### Health2000
nb_removed_h2000 <- h2000_nb_women_no_menstruation_response +
                            h2000_summary_final %>% filter(is.na(BMI)) %>% nrow() 

h2000_summary_final <- h2000_summary_final %>% 
  filter(!is.na(BMI))

Smoking

We remove “cohort_name %>% filter(is.na(BMI)) %>% nrow()” participants for whom we do not have smoking data: * FinDonor: 1 * FinRisk97: 54 * Health2000: 15

# FinDonor
nb_removed_blood <- nb_removed_blood +
                            blood_data_summary_final %>% filter(is.na(Smoking)) %>% nrow() 

blood_data_summary_final <- blood_data_summary_final %>% 
  filter(!is.na(Smoking))

# FinRisk97
nb_removed_fr97 <- nb_removed_fr97 +
                            fr1997_summary_final %>% filter(is.na(Smoking)) %>% nrow()

fr1997_summary_final <- fr1997_summary_final %>%
  filter(!is.na(Smoking))

# Health2000
h2000_summary_final$Smoking[is.na(h2000_summary_final$Smoking)]  <- 3

nb_removed_h2000 <- nb_removed_h2000 +
                            h2000_summary_final %>% filter(is.na(Smoking)) %>% nrow()

h2000_summary_final <- h2000_summary_final %>%
  filter(!is.na(Smoking))

Previous childbirth

We remove “cohort_name %>% filter((group !=”Men" & is.na(PreviousChildbirth)))%>% nrow()" female donors who did not answer the question on childbirth: * FinDonor: 3 * FinRisk97: 14 * Health2000: 6

#### FinDonor
nb_removed_blood <- nb_removed_blood +
                            blood_data_summary_final %>% filter((Group != "Men" & is.na(PreviousChildbirth))) %>% nrow 

blood_data_summary_final <- blood_data_summary_final %>% 
  filter(!(Group != "Men" & is.na(PreviousChildbirth)))

#### FinRisk97
nb_removed_fr97 <- nb_removed_fr97 +
                            fr1997_summary_final %>% filter((Group != "Men" & is.na(PreviousChildbirth))) %>% nrow 

fr1997_summary_final <- fr1997_summary_final %>% 
  filter(!(Group != "Men" & is.na(PreviousChildbirth)))

#### Health2000
nb_removed_h2000 <- nb_removed_h2000 +
                            h2000_summary_final %>% filter((Group != "Men" & is.na(PreviousChildbirth))) %>% nrow 

h2000_summary_final <- h2000_summary_final %>% 
  filter(!(Group != "Men" & is.na(PreviousChildbirth)))

Current pregnancy

Only women <45 yrs were asked whether they are currently pregnant and if so, how many weeks (source: https://www.julkari.fi/bitstream/handle/10024/78181/2005b6.pdf?sequence=1&isAllowed=y, page 56). We added a filter for age <45 years old and imputed NA:s in >45-year-oldes as not pregnant. Although pregnancy is possible after this age, chances are much lower. We also used this age as our cut-off for menopause.

We will now remove “cohort_name %>% filter((Group !=”Men" & is.na(CurrentPregnancy)))%>% nrow()" females who did not answer the pregnancy question: * FinRisk97: 8 * Health00: 3

# FinRisk97
nb_removed_fr97 <- nb_removed_fr97 +
                            fr1997_summary_final %>% filter((Group != "Men" & is.na(CurrentPregnancy))) %>% nrow 

fr1997_summary_final <- fr1997_summary_final %>% 
  filter(!(Group != "Men" & is.na(CurrentPregnancy)))

# Health2000

# check age variance 
table(h2000_summary_final$CurrentPregnancy, h2000_summary_final$Age)

##    
##     30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
##   0 41 58 84 65 66 61 60 69 60 71 54 63 66 76 57 67 64 56 63 49 41 30 33 33 21
##   1  1  3  6  2  4  2  1  3  2  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
##    
##     55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70
##   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##   1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0

nb_removed_h2000 <- nb_removed_h2000 +
                            h2000_summary_final %>% filter((Group != "Men" & Age <=45 & is.na(CurrentPregnancy))) %>% nrow 

h2000_summary_final <- h2000_summary_final %>% 
  filter(!(Group != "Men" & Age <=45 & is.na(CurrentPregnancy)))

h2000_summary_final$CurrentPregnancy[is.na(h2000_summary_final$CurrentPregnancy)]  <- 0

Iron supplementation

We remove 77 donors that did not answer the two questions (did they receive iron supplements at during their last donation and supplement complience)

For the modelling, we impute a 0 (no supplementation) to iron_comp_c when the donor reports not being offered iron supplementation.

This is not done for the general population cohorts, as they were not asked about iron supplementation and as such all observation are input as 0 (these questions specifically concern iron supplements provided in conjuction with blood donation).

nb_removed_blood <- nb_removed_blood +
                            blood_data_summary_final %>% 
                              mutate(IronComplienceNumeric = ifelse(GivenIronSupplements == FALSE, 0, IronComplienceNumeric)) %>% 
                              filter(is.na(IronComplienceNumeric)) %>% 
                              nrow() 

blood_data_summary_final <- blood_data_summary_final %>% 
  mutate(IronComplienceNumeric = ifelse(GivenIronSupplements == FALSE, 0, IronComplienceNumeric )) %>% 
  filter(!is.na(IronComplienceNumeric))

Red meat intake

We remove “cohort_name %>% filter(is.na(RedMeat)) %>% nrow()” participants for whom we do not have Smoking data: * Findonor: 10 * FinRisk97: 21

Health2000 participants were not asked about red meat intake.

# FinDonor
donors_to_remove <- blood_data_summary_final %>%
  dplyr::select(ID, Group, RedMeat) %>%
  gather(key= question, value = answer, -ID,-Group) %>%
  filter(is.na(answer)) %>%
  dplyr::select(Group, ID) %>%
  distinct(ID, Group)

blood_data_summary_final <- blood_data_summary_final %>%
  filter(!ID %in% donors_to_remove$ID)

nb_removed_blood <- nb_removed_blood +
                            donors_to_remove %>% nrow()

# FinRisk97
finrisk97_to_remove <- fr1997_summary_final %>%
  dplyr::select(ID, Group, RedMeat) %>%
  gather(key= question, value = answer, -ID,-Group) %>%
  filter(is.na(answer)) %>%
  dplyr::select(Group, ID) %>%
  distinct(ID, Group)

fr1997_summary_final <- fr1997_summary_final %>%
  filter(!ID %in% finrisk97_to_remove$ID)

nb_removed_fr97 <- nb_removed_fr97 +
                            finrisk97_to_remove %>% nrow()

Age

We remove “cohort_name %>% filter(is.na(Age)) %>% nrow()” participants with unknown age: * FinDonor: 0 * FinRisk97: 0 * Health2000: 0

#### FinDonor
nb_removed_blood <- findonor_nb_women_no_menstruation_response +
                            blood_data_summary_final %>% filter(is.na(Age)) %>% nrow() 

blood_data_summary_final <- blood_data_summary_final %>% 
  filter(!is.na(Age))

#### FinRisk97
nb_removed_fr97 <- finrisk_nb_women_no_menstruation_response +
                            fr1997_summary_final %>% filter(is.na(Age)) %>% nrow() 

fr1997_summary_final <- fr1997_summary_final %>% 
  filter(!is.na(Age))

#### Health2000
nb_removed_h2000 <- h2000_nb_women_no_menstruation_response +
                            h2000_summary_final %>% filter(is.na(Age)) %>% nrow() 

h2000_summary_final <- h2000_summary_final %>% 
  filter(!is.na(Age))

The total number removed because of missing questionnaire data is: 14 9 180

Remove cohort participants who are currently pregnant

As current pregnancy was an exclusion criteria for the NL general population cohort and pregnant women are not allowed to donate blood, we will remove women who are currently pregnant from the FIN general population cohorts. The number of removed participants is

FinRisk97: 69
Health00: 25

FinRisk97: 1=no, 2=yes Health2000: 0=no, 1=yes

We will also recode the FinRisk97 cohort answer of 1=no to 0=no.

# FinRisk97
nb_removed_pregnant_fr1997 <- fr1997_summary_final %>% 
   filter(CurrentPregnancy == 2) %>% 
  nrow()

fr1997_summary_final <- fr1997_summary_final %>% 
   filter(!(Group != "Men" & CurrentPregnancy == 2))


fr1997_summary_final$CurrentPregnancy[fr1997_summary_final$CurrentPregnancy == "1"] <- 0

#Health2000
nb_removed_pregnant_h2000 <- h2000_summary_final %>% 
   filter((CurrentPregnancy == 1)) %>% 
  nrow()

h2000_summary_final <- h2000_summary_final %>% 
   filter(!(Group != "Men" & CurrentPregnancy == 1))

Remove participants with extreme physiological measures

As decided previously, we remove data according the following criteria:

BMI > 50
Ferritin > 400
Weight < 50kg (blood donor selection criteria)

This amounts to “cohort_name %>% filter(BMI >= 50 | Ferritin >= 400 | Weight < 50) %>% nrow()” participants that are removed. * FinDonor: 12 * FinRisk97: 279 * Health00: 179

# FinDonor
blood_data_summary_final <- blood_data_summary_final %>% 
   filter(BMI < 50 & Ferritin < 400 & Weight >= 50)

# FinRisk97
fr1997_summary_final <- fr1997_summary_final %>% 
   filter(BMI < 50 & Ferritin < 400 & Weight >= 50)

#Health2000
h2000_summary_final <- h2000_summary_final %>% 
   filter(BMI < 50 & Ferritin < 400 & Weight >= 50)

Final group N for the three cohorts

# FinDonor
blood_data_summary_final %>% 
   mutate(Group = dplyr::recode(Group, Pre_menopause_women = "Pre-menopausal women",
         Post_menopause_women = "Post-menopausal women")) %>% 
  group_by(Group) %>% 
  summarise ( N = n()) %>% 
  kable()

Group	N
Pre-menopausal women	877
Post-menopausal women	492
Men	954

# FinRisk97
fr1997_summary_final %>% 
   mutate(Group = dplyr::recode(Group, Pre_menopause_women = "Pre-menopausal women",
         Post_menopause_women = "Post-menopausal women")) %>% 
  group_by(Group) %>% 
  summarise ( N = n()) %>% 
  kable()

Group	N
Pre-menopausal women	2155
Post-menopausal women	1248
Men	3434

# Health2000
h2000_summary_final %>% 
   mutate(Group = dplyr::recode(Group, Pre_menopause_women = "Pre-menopausal women",
         Post_menopause_women = "Post-menopausal women")) %>% 
  group_by(Group) %>% 
  summarise ( N = n()) %>% 
  kable()

Group	N
Pre-menopausal women	1359
Post-menopausal women	1029
Men	2385

Recode variables

Red meat

Next we recode red meat consumption into a linear variable. We use a linear scale from 1 to 4 for and mapped the different responses to correspond to the four options for the FinDonor and FinRisk97 cohorts. Data on red meat consumption was not avaliable in the Health2000 cohort.

red meat:
- FinDonor
  - “never” ~ 1, # never
  - “less_than_once_weekly” ~ 2, # less than once a week
  - “1.3_week” or “4.6_week”~ 3, # 1-3 times a week # 4-6 times a week
  - “daily” or “several_daily” ~ 4, # daily # several times a day
- FinRisk97
  - “1” ~ 1, # 1 = more seldom than once a month or not at all
  - “2” ~ 2, # 2 = once or twice a month
  - “3” or “4” or “5” ~ 3, # 3 = once a week # 4 = twice a week or more often # 5 = almost daily
  - “5” or “several_daily” ~ 4, # 6 = daily # 7 = several times a day

This will create a new variable, RedMeat_n. As we don’t have red meat data in the Health2000 cohort, we need to make a column, name it RedMeat_n and impute the observations as n/a.

# FinDonor
blood_data_summary_final <- blood_data_summary_final %>% 
  mutate(RedMeat_n = case_when(
    RedMeat == "never" ~ 1,
    RedMeat == "less_than_once_weekly" ~ 2,
    RedMeat %in% c("1.3_week" , "4.6_week") ~ 3,
    TRUE   ~ 4 )) 

# FinRisk97
fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(RedMeat_n = case_when(
    RedMeat == "1" ~ 1,
    RedMeat == "2" ~ 2,
    RedMeat %in% c("3" , "4", "5") ~ 3,
    TRUE   ~ 4 )) # "6" | "7"
  
# Health2000
h2000_summary_final$RedMeat_n <- NA

CRP

Due to difference in the CRP measurements (hs-CRP for the general population cohorts, CRP for the blood donor cohort) we decided to impute CRP <3 mg/l to 2.9 mg/l for the general population cohorts in order to align them with the blood donor data. CRP <3 mg/l was previously imputed as 2.9 mg/ml in the blood donor cohort.

# FinRisk97
fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(CRPori = CRP) # Save original CRP for later use
fr1997_summary_final$CRP[fr1997_summary_final$CRP < 3.0] <- 2.9

# Health2000
h2000_summary_final <- h2000_summary_final %>% 
  mutate(CRPori = CRP) # Save original CRP for later use

h2000_summary_final$CRP[h2000_summary_final$CRP < 3.0] <- 2.9

#Blood donors
blood_data_summary_final <- blood_data_summary_final %>% 
  mutate(CRPori = CRP) 
# Save original CRP for later use, this is needed as we will concatenate this later

Previous childbirth

Previous childbirth is recoded * FinDonor: have you given birth? no=no, yes=yes * FinRisk97: how many children have you given birth to? 1=none, 2=one, 3=two, 4=three or more * Health2000: have you given birth? 0=no, 1=yes

Nulliparous women are coded as 0 (no) and reporting childbirth or giving birth to any number of children as 1 (yes).

# FinDonor
blood_data_summary_final <- blood_data_summary_final %>% 
  mutate(PreviousChildbirth = case_when(
    PreviousChildbirth == "no" ~ 0,
    PreviousChildbirth == "yes" ~ 1)) 

# FinRisk97
fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(PreviousChildbirth = case_when(
    PreviousChildbirth == "1" ~ 0,
    PreviousChildbirth %in% c("2" , "3", "4") ~ 1))

Regions

Assign the participants to regions based on university hospital districts. FinDonor study participants all come from the capital region.

Health2000: 1 HYKS
2 TYKS 3 TAYS 4 KYS 5 OYS

FinRisk97: 2 North Karelia –> KYS 3 North Savonia –> KYS 4 Turku and Loimaa –> TYKS 5 Helsinki and Vantaa –> HYKS 6 Oulu province –> OYS

blood_data_summary_final$Region <- "HYKS"

fr1997_summary_final <- fr1997_summary_final %>% 
  mutate(Region = case_when(
    Region == "2" ~ "KYS",
    Region == "3" ~ "KYS",
    Region == "4" ~ "TYKS",
    Region == "5" ~ "HYKS",
    Region == "6" ~ "OYS"))

h2000_summary_final <- h2000_summary_final %>% 
  mutate(Region = case_when(
    Region == "1" ~ "HYKS",
    Region == "2" ~ "TYKS",
    Region == "3" ~ "TAYS",
    Region == "4" ~ "KYS",
    Region == "5" ~ "OYS"))

Merging cohorts into one table

For the code to work all three cohorts need to be merged into one single table. Before this can be done variables the variables selected need to have the same name.

### mutate smoking and pregnancy 
blood_data_summary_final <- blood_data_summary_final %>% 
  dplyr::select(ID, Group, Sex, Age, TwoYearsFromStartCount_FB, DaysToPreviousFB,
                Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, IronComplienceNumeric, PreviousChildbirth,
                CRP, CRPori, Region, Weight, Cohort) %>% 
    mutate(Smoking = ifelse(Smoking == "daily", "yes", "no"),
         Smoking = factor(Smoking, levels = c( "no",  "yes")))

### mutate smoking and pregnancy 
fr1997_summary_final <- fr1997_summary_final %>% 
  dplyr::select(ID, Group, Sex, Age, TwoYearsFromStartCount_FB, DaysToPreviousFB,
                Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, IronComplienceNumeric, PreviousChildbirth,
                CRP, CRPori, Region, Weight, Cohort) %>% 
       mutate(Smoking = ifelse(Smoking == "1", "yes", "no"),
       Smoking = factor(Smoking, levels = c( "no",  "yes")))

### mutate smoking and pregnancy
h2000_summary_final <- h2000_summary_final %>% 
  dplyr::select(ID, Group, Sex, Age, TwoYearsFromStartCount_FB, DaysToPreviousFB,
                Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, IronComplienceNumeric, PreviousChildbirth,
                CRP, CRPori,Region, Weight, Cohort) %>% 
         mutate(Smoking = ifelse(Smoking == "1", "yes", "no"),
         Smoking = factor(Smoking, levels = c( "no",  "yes")))
         

# merge the cohorts

summary_all_cohorts <- bind_rows(blood_data_summary_final, fr1997_summary_final, h2000_summary_final)

file <- "../results/summary_all_cohorts.rdata"
save(summary_all_cohorts,file=file)

Final group N for the summarized table

summary_all_cohorts %>% 
   mutate(Group = dplyr::recode(Group, Pre_menopause_women = "Pre-menopausal women",
         Post_menopause_women = "Post-menopausal women")) %>% 
  group_by(Group) %>% 
  summarise ( N = n()) %>% 
  kable()

Group	N
Pre-menopausal women	4391
Post-menopausal women	2769
Men	6773

Table 1

myVars <- c("Age" ,
  "Ferritin (ug/l)",
  "Hb (g/l)",
  "BMI"
)
non_normal_vars <- c("Ferritin (ug/l)")
table1data <- summary_all_cohorts  %>%
  rename(
  "Age" = Age,
  "Ferritin (ug/l)" = Ferritin,
  "Hb (g/l)" = Hb,
  "BMI" = BMI
)

summary_table <- CreateTableOne(data = 
                                  table1data,
                                vars=myVars, 
                                strata = c("Cohort","Sex"),
                                test = FALSE)

## Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf

## Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning -Inf

## Warning in StdDiff(variable = var, group = strataVar): Variable has only NA's in
## at least one stratum. na.rm turned off.

tab3Mat <- print(summary_table, 
                 nonnormal = non_normal_vars,
                 vars=myVars, 
                 quote = FALSE, 
                 noSpaces = TRUE, 
                 printToggle = FALSE)
#
colnames(tab3Mat) <- gsub("\\:",": ",colnames(tab3Mat))
tab3Mat %>% 
  kable() %>% 
kable_styling(
  full_width = F,
  bootstrap_options = "striped", 
  font_size = 8) %>% 
  column_spec(
    column = 2:7,
    width = '2.5cm'
  )

	FINDONOR: Men	FINRISK97: Men	HEALTH00: Men	FINDONOR: Women	FINRISK97: Women	HEALTH00: Women
n	954	3434	2385	1369	3403	2388
Age (mean (SD))	46.05 (13.69)	48.39 (12.85)	48.12 (10.97)	42.77 (14.41)	46.80 (12.18)	49.05 (11.37)
Ferritin (ug/l) (median [IQR])	43.00 [25.00, 69.00]	106.10 [63.15, 171.99]	116.80 [71.40, 174.36]	29.00 [17.00, 45.00]	33.52 [16.42, 62.89]	36.11 [17.20, 68.37]
Hb (g/l) (mean (SD))	150.52 (9.31)	150.85 (10.93)	151.62 (11.71)	136.53 (8.49)	NaN (NA)	136.09 (12.18)
BMI (mean (SD))	26.39 (3.92)	26.83 (3.81)	26.99 (3.98)	25.82 (4.89)	26.34 (4.85)	26.70 (4.96)

  write.table(tab3Mat, 
              file = paste0("../results/low_ferritin_data/table_1.txt"),sep="\t")

Results - Regression table

Rename and transform

regression_cohorts <- summary_all_cohorts %>% 
    rename(donation_count = TwoYearsFromStartCount_FB,
            last_donation = DaysToPreviousFB,
            iron_complience = IronComplienceNumeric) %>% 
     mutate(Age = Age / 5, 
            Weight = Weight / 10,
            donation_count_2 = donation_count^2,
            log_ferritin = log(Ferritin),
            log_last_donation = log(last_donation)/log(2),
            log_CRP = log(CRP),
            iron_deficiency = Ferritin < 15)

Save table

write.table(regression_cohorts, file = paste0("../results/regressioncohorts",".txt"))

save(regression_cohorts, file = "../data/ID_data_regression_cohorts.rdata")

Data transformations:

Log transformed variables:
- Ferritin
- CRP
Age is divided by 5 and weight is devided by 10 to simplify coefficient interpretation
Number of days before donation is transformed as \(log\_last\_donation = log(last\_donation)/log(2)\) to help with the interpretation of coefficients.
- An increase in one of the transformed variable is equivalent to a doubling of the number of days since last donation.
Standardization:
- Standardized coefficients:
  - All dependent and independent variables were standardized
- Coefficients:
  - All dependent variables entered as continuous varaibles are centered but not scaled.

We center all variables entered as continuous.

Premenopausal women

test_data_pre_women <- regression_cohorts %>% 
  filter(Group == "Pre_menopause_women")

test_data_pre_women <- test_data_pre_women %>% 
  dplyr::select(ID, Group, Sex, Age, donation_count, last_donation, Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, iron_complience, PreviousChildbirth, CRP, Cohort, donation_count_2, log_ferritin, log_last_donation, log_CRP, iron_deficiency, Region, Weight) %>%
  mutate(Age = scale(Age, scale = FALSE)[,1],
         Weight = scale(Weight, scale = FALSE)[,1],
         log_CRP = scale(log_CRP, scale = FALSE)[,1],
         donation_count = scale(donation_count, scale = FALSE)[,1],
         log_last_donation = scale(log_last_donation, scale = FALSE)[,1],
         BMI = scale(BMI, scale = FALSE)[,1],
         donation_count_2 = donation_count^2)

Save table

write.table(test_data_pre_women, file = paste0("../results/test_data_pre_women",".txt"))

Postmenopausal women

test_data_post_women <- regression_cohorts %>% 
  filter(Group == "Post_menopause_women") 

test_data_post_women <- test_data_post_women %>% 
  dplyr::select(ID, Group, Sex, Age, donation_count, last_donation, Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, iron_complience, PreviousChildbirth, CRP, Cohort, donation_count_2, log_ferritin, log_last_donation, log_CRP, iron_deficiency, Region, Weight) %>%
  mutate(Age = scale(Age, scale = FALSE)[,1],
         Weight = scale(Weight, scale = FALSE)[,1],
         log_CRP = scale(log_CRP, scale = FALSE)[,1],
         donation_count = scale(donation_count, scale = FALSE)[,1],
         log_last_donation = scale(log_last_donation, scale = FALSE)[,1],
         BMI = scale(BMI, scale = FALSE)[,1],
         donation_count_2 = donation_count^2)

Save table

write.table(test_data_post_women, file = paste0("../results/test_data_post_women",".txt"))

Men

test_data_men <- regression_cohorts %>% 
  filter(Group == "Men") 

test_data_men <- test_data_men %>% 
  dplyr::select(ID, Group, Sex, Age, donation_count, last_donation, Ferritin, Hb, BMI, Menstruation, Smoking, RedMeat_n, iron_complience, PreviousChildbirth, CRP, Cohort, donation_count_2, log_ferritin, log_last_donation, log_CRP, iron_deficiency, Region, Weight) %>%
  mutate(Age = scale(Age, scale = FALSE)[,1],
         Weight = scale(Weight, scale = FALSE)[,1],
         log_CRP = scale(log_CRP, scale = FALSE)[,1],
         donation_count = scale(donation_count, scale = FALSE)[,1],
         log_last_donation = scale(log_last_donation, scale = FALSE)[,1],
         BMI = scale(BMI, scale = FALSE)[,1],
         donation_count_2 = donation_count^2)

Save table

write.table(test_data_men, file = paste0("../results/test_data_men",".txt"))

Mutiple regression - ferritin as outcome

Correlograms

# Premenopausal women
ggpairs(test_data_pre_women, 
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE)

# Postmenopausal women
ggpairs(test_data_post_women, 
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE)

# Men
ggpairs(test_data_men, 
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE)

#same as above, cohorts separated 

# Premenopausal women
ggpairs(test_data_pre_women, ggplot2::aes(colour = Cohort, alpha = 0.5),
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE) + 
  ggtitle("Premenopausal women")

# Postmenopausal women
ggpairs(test_data_post_women, ggplot2::aes(colour = Cohort, alpha = 0.5),
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE) +
  ggtitle("Postmenopausal women")

# Men
ggpairs(test_data_men, ggplot2::aes(colour = Cohort, alpha = 0.5),
        columns = c("log_ferritin", "iron_deficiency", "Age", "log_CRP",  "donation_count", "donation_count_2",
                    "log_last_donation"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE) + 
ggtitle("Men")

ggpairs(test_data_pre_women, ggplot2::aes(colour = Cohort, alpha = 0.5),
        columns = c("log_ferritin", "iron_deficiency", "PreviousChildbirth",  "RedMeat_n", "Smoking", "iron_complience", "BMI", "Menstruation", "Region", "Weight"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE) + 
ggtitle("Premenopausal women")

ggpairs(test_data_post_women, ggplot2::aes(colour = Cohort, alpha = 0.5), 
        columns = c("log_ferritin", "iron_deficiency", "PreviousChildbirth",  "RedMeat_n", "Smoking", "iron_complience", "BMI", "Region", "Weight"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE)+ 
ggtitle("Postmenopausal women")

ggpairs(test_data_men, ggplot2::aes(colour = Cohort, alpha = 0.5),
        columns = c("log_ferritin", "iron_deficiency",  "RedMeat_n", "Smoking", "iron_complience", "BMI", "Region", "Weight"),
         lower = list(continuous = wrap("points", alpha = 0.3,size=0.1),
                      combo = wrap("facethist", binwidth = 0.5)),
        progress = FALSE) + 
ggtitle("Men")

Low ferritin data

Sofie Ekroos

19.7.2021

Summary

Data loading and preparation

Data loading

Data preparation

Demographic group specification and assignment (coninued after “figuring out why so many Health2000 participants are removed” section)

Figuring out why so many Health2000 participants are removed (skip this part unless there are problems with the cohort)

Demographic group specification and assignment continued

Remove cohort participants with missing data

Donation history

BMI

Smoking

Previous childbirth

Current pregnancy

Iron supplementation

Red meat intake

Age

Remove cohort participants who are currently pregnant

Remove participants with extreme physiological measures

Final group N for the three cohorts

Recode variables

Red meat

CRP

Previous childbirth

Regions

Merging cohorts into one table

Final group N for the summarized table

Table 1

Results - Regression table

Rename and transform

Premenopausal women

Postmenopausal women

Men

Mutiple regression - ferritin as outcome

Correlograms